Petrochemical product suppliers often produce product at a number of plant production locations and distribute product using a common distribution system that connects multiple production plants to multiple customers. For example, hydrogen, carbon monoxide, and syngas are typically produced in synthesis gas plants in various locations, often close to raw material sources. These synthesis gas plants are often linked together and to multiple customers via a common distribution system, such as a pipeline system or a series of pipelines. The common distribution system may be made of several sub-distribution systems that are or are physically separate from the main distribution system.
Product in the distribution system can come from any one of the multiple production units; thus, a system must be used to allocate production between the various production units. The efficient allocation of production can be very complex due to the variable costs, such as raw materials, energy, capacities, operating efficiencies, or plant availability on any given day.
Production planning is important to providing product to customers while maintaining profitability. Product in the distribution system can be used by any of a number of customers, and at varying rates. Furthermore, raw material supply contracts, such as natural gas and electricity, typically include take-or-pay arrangements, variable pricing based on usage and energy market, and/or price penalties for exceeding contracted usage rates. Production demand is allocated among multiple production plants based on system inventory, plant availability, plant efficiencies, variable costs, and expected customer demand. Prior art production planning methods typically use a linear model based on monthly average cost to produce a production plan. Furthermore, typical prior art methods assume that raw material costs are constant over varying production rates. Thus, the prior art methods do not necessarily minimize the cost of production.
In many petrochemical distribution systems, the demand at any given time can vary from the target contracted amount and can change almost instantly. Thus, many distribution networks are monitored for real-time demand on the system by monitoring parameters, such as pipeline pressure or flow. The distribution system must maintain a “network buffer” (i.e.: a reserve capacity or extra storage of product) to respond to any changes in customer demand. A properly controlled distribution system will maintain sufficient product flows and pressure to supply all customers and assure that no customer loses supply. While traditional approaches to production planning assure that customer demands are met by maintaining a sufficiently large enough network buffer and overproducing product, they do not guarantee that the buffer will be maintained with the lowest operating cost.
Profitability of networked production operations is driven by allocation of production to the various plants in such a manner as to minimize the raw material and energy costs, which are typically the major variable cost components of the system overall. It can be a complex and daunting task to profitably allocate production, as variable costs can vary between plants, and can vary from day to day in each plant. As a result, many networked production systems are operated based on the average variable cost of a plant, which will not minimize the variable cost across the system for a given time period.
Profitability is also significantly affected by additional production demand that results in variations in variable costs. The additional production demand may be due to unplanned consumption by the customer, customers exceeding contracted usage, or other circumstances. Providing product to meet the addition production demand can result in deviations from raw material supply contracts. This can result in substantial, usually negative, variable cost impacts as the production plant is forced to deviate from the contracted amounts for raw material to support the additional production demand. This deviation in variable cost can also vary between plant locations. Traditional production planning systems do not adequately address the affects of this additional production demand on production planning and the difference between operating plants in the costs created by the additional demand.
In light of the foregoing problems associated with minimizing variable costs, a need exists for an improved method of production planning, wherein the variable costs of each plant in a networked system can be monitored and production plans produced that minimize the variable cost across the network over a period of time (a time step) as the production demand varies. Furthermore, there is a need for a production planning system, wherein costs for base contract production and differential costs created by differential production demand can be monitored and minimized across the total distribution network. A need also exists to facilitate reporting and simulate future investments and evolution on the network.